Nonlinear deconvolution method

Abstract

The invention provides a nonlinear deconvolution method. The method comprises the steps that post-stack seismic data are divided into a plurality of time windows, and the following steps are conducted on each time window: (a) mixed phase seismic wavelets w (t) are extracted in a tri-spectrum domain; (b) on the basis of a target function obtained through a seismic convolution model and the Bayesian theory, improved Kexi criterions are adopted for establishing weak reflection sparse constraints, VSP seismic data are introduced to serve as prior constrains of reflection coefficients, and a reflection coefficient target function is established; (c) the reflection coefficient target function is solved through the mixed phase seismic wavelets, and the accurate reflection coefficients are obtained; (d) convolution is conducted on the solved reflection coefficients and high-frequency zero-phase wavelets, and the seismic data obtained after the resolution is improved are formed. According to the nonlinear deconvolution method, the limitation of effective frequency bands of the seismic data is broken through, the capability for distinguishing thin interbeds through the seismic data is improved, and the resolution of the seismic data is obviously improved.

Description

technical field [0001] The invention relates to the field of petroleum geological exploration, more specifically, to a method for improving the resolution of seismic data. Background technique [0002] Deconvolution is the main method to improve the resolution of seismic records, and plays an important role in the high-resolution processing of actual seismic data. However, with the development of exploration and development, seismic exploration has shifted from simple structural exploration to exploration of complex surface, complex structures, fractures, thin reservoirs and remaining oil in old oilfields. The exploration of remaining oil in thin reservoirs and old oilfields is directly related to the resolution of seismic imaging. The traditional deconvolution method based on linear Wiener filtering can no longer meet the requirements of seismic data resolution for thin layer identification. [0003] The ability of the traditional deconvolution method to improve the resolu...

AI Technical Summary

Problems solved by technology

By introducing sparsity constraints, the resolution of seismic records has been greatly improved, but it has also caused some negative effects: one is the multi-solution of deconvolution results, the other is the suppression effect on weak reflections, and the third is how to extract high-precision The mixed phase wavelet of this type of method is an inherent defect that cannot be eliminated by this type of method, and it is also the price of breaking through the effective frequency band of seismic data.

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